ArticleObesity surgery2025
Artificial Intelligence in Bariatric Surgery: Optimizing Personalized Decision-Making, Predictive Monitoring, and Postoperative Outcomes.
Article in Obesity surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Preoperative Glucagon-Like Peptide-1 Receptor Agonists and Bariatric Surgery Outcomes: A Systematic Review.Obesity surgery · 2025Pooled it
- Predictors of same-day discharge following bariatric surgery using machine learning.Surgical endoscopy · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bariatric surgery is an effective treatment for morbid obesity, but patient outcomes differ greatly because of a variety of phenotypes, comorbidities, and postoperative adherence. In bariatric care, artificial intelligence (AI) and machine learning (ML) are becoming revolutionary tools because traditional predictive models based on BMI and demographic variables are unable to account for these complexities. To put it simply, AI is a branch of computer science that enables machines to perform tasks that typically require human intelligence. On the other hand, ML is a subset of AI, where systems learn from data to improve predictions. This study investigates how AI can be used to enhance dynamic, patient-centered follow-up, predict postoperative complications, and improve surgical decision-making. AI can customize interventions, lower complications, and promote long-term weight loss by combining multidimensional data, such as metabolic profiles, behavioral feedback, and phenotypic traits. This study demonstrates how precision powered by AI is laying the groundwork for bariatric surgery in the future.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.